Exploiting Social Network for Forensic Analysis to Predict Civil Unrest

2016 ◽  
Vol 5 (1) ◽  
pp. 61-68
Author(s):  
Ruchika Parmarth GANAR ◽  
Shrikant B. ARDHAPURKAR
Keyword(s):  
Social Network ◽  
Forensic Analysis ◽  
Civil Unrest

Multilevel Visualization Using Enhanced Social Network Analysis with Smartphone Data

2013 ◽  
Vol 5 (4) ◽  
pp. 34-54 ◽  
Author(s):  
Panagiotis Andriotis ◽  
Zacharias Tzermias ◽  
Anthi Mparmpaki ◽  
Sotiris Ioannidis ◽  
George Oikonomou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Direct Marketing ◽  
Forensic Analysis ◽  
Three Dimensions ◽  
Fully Integrated ◽  
Aggregate Information ◽  
The Social ◽  
Correlated Information

While technology matures and becomes more productive, mobile devices can be affordable and, consequently, fully integrated in people's lives. After their unexpected bloom and acceptance, Online Social Networks are now sources of valuable information. The authors therefore use them for tasks varying from direct marketing to forensic analysis. The authors have already seen Social Network Forensics techniques focused on particular networks implementing methods that collect data from user accounts. During the forensic analysis it is common to aggregate information from different sources but, usually, this procedure causes correlation problems. Here, the authors present their method to correlate data gathered from various social networks in combination with smartphones creating a new form of social map of the user under investigation. In addition, the authors introduce a multi level graph that utilises the correlated information from the smartphone and the social networks and demonstrates in three dimensions the relevance of each contact with the suspect.

scholarly journals Construction and Performance Analysis of Image Steganography-based Botnet in KakaoTalk Openchat

Computers ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 61 ◽  
Author(s):  
Jeon ◽  
Cho
Keyword(s):  
Social Network ◽  
Forensic Analysis ◽  
Image Steganography ◽  
Botnet Detection ◽  
Social Network Service ◽  
Detection Techniques ◽  
Detection Systems ◽  
Digital Forensic ◽  
Communication Reliability ◽  
And Performance

Once a botnet is constructed over the network, a bot master and bots start communicating by periodically exchanging messages, which is known as botnet C&C communication, in order to send botnet commands to bots, collect critical information stored in bots, upgrade software functions of malwares installed in bots, and so on. For this reason, most existing botnet detection techniques focus on monitoring and capturing suspicious communications between the bot master and bots. Meanwhile, botnets continue to evolve to hide their C&C communication. Recently, a novel type of botnet using image steganography techniques and SNS (Social Network Service) platforms, which is known as image steganography-based botnet or stegobotnet, has emerged to make its C&C communications undetectable by existing botnet detection systems. In stegobotnets, image files used in SNSs carry messages (between the bot master and bots) which are hidden in them by using image steganography techniques. In this paper, we first investigate whether major SNS platforms such as KakaoTalk, Facebook, and Twitter can be suitable for constructing image steganography-based botnets. Next, we construct a part of stegobotnet based on KakaoTalk, and conduct extensive experiments including digital forensic analysis (1) to validate stegobotnet C&C communication can be successful in KakaoTalk and (2) to examine its performance in terms of C&C communication reliability.

Forensic applications of IR infrared microscopic internal reflection spectroscopy

1992 ◽  
Vol 50 (2) ◽  
pp. 1528-1529
Author(s):  
Edward G. Bartick ◽  
John A. Reffner
Keyword(s):  
Specular Reflection ◽  
Forensic Analysis ◽  
Attenuated Total Reflection ◽  
Internal Reflection ◽  
Standard Size ◽  
Ir Microscopy ◽  
Ir Analysis ◽  
Plastic Explosives ◽  
Internal Reflection Spectroscopy ◽  
Reflectance Measurements

Since the introduction of commercial Fourier transform infrared (FTIR) microscopic systems in 1983, IR microscopy has developed as an important analytical tool in research, industry and forensic analysis. Because of the frequent encounter of small quantities of physical evidence found at crime scenes, spectroscopic IR microscopes have proven particularly valuable for forensic applications. Transmittance and reflectance measurements have proven very useful. Reflection-absorption, specular reflection, and diffuse reflection have all been applied. However, it has been only very recently that an internal reflection (IRS) objective has been commercially introduced.The IRS method, also known as attenuated total reflection (ATR), has proven very useful for IR analysis of standard size samples. The method has been applied to adhesive tapes, plastic explosives, and general applications in the analysis of opaque materials found as evidence. The small quantities or uncontaminated areas of specimens frequently found requiring forensic analysis will often be directly applicable to microscopic IRS analysis.

The social network

2013 ◽  
Vol 44 (2) ◽  
pp. 22
Author(s):  
ALAN ROCKOFF
Keyword(s):  
Social Network ◽  
The Social

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

Extraverts Have Larger Social Network Layers

2011 ◽  
Vol 32 (3) ◽  
pp. 161-169 ◽  
Author(s):  
Thomas V. Pollet ◽  
Sam G. B. Roberts ◽  
Robin I. M. Dunbar
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Outer Layer ◽  
Network Size ◽  
Emotional Intensity ◽  
Emotional Closeness ◽  
Network Layer ◽  
Network Layers ◽  
Social Network Size

Previous studies showed that extraversion influences social network size. However, it is unclear how extraversion affects the size of different layers of the network, and how extraversion relates to the emotional intensity of social relationships. We examined the relationships between extraversion, network size, and emotional closeness for 117 individuals. The results demonstrated that extraverts had larger networks at every layer (support clique, sympathy group, outer layer). The results were robust and were not attributable to potential confounds such as sex, though they were modest in size (raw correlations between extraversion and size of network layer, .20 < r < .23). However, extraverts were not emotionally closer to individuals in their network, even after controlling for network size. These results highlight the importance of considering not just social network size in relation to personality, but also the quality of relationships with network members.

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

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